from caffe2.python import core, workspace
from hypothesis import assume, given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np


class TestReductionOps(serial.SerializedTestCase):

    @serial.given(n=st.integers(5, 8), **hu.gcs)
    def test_elementwise_sum(self, n, gc, dc):
        X = np.random.rand(n).astype(np.float32)

        def sum_op(X):
            return [np.sum(X)]

        op = core.CreateOperator(
            "SumElements",
            ["X"],
            ["y"]
        )

        self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=sum_op,
        )

        self.assertGradientChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            outputs_to_check=0,
            outputs_with_grads=[0],
        )

    @given(n=st.integers(5, 8), **hu.gcs)
    @settings(deadline=10000)
    def test_elementwise_int_sum(self, n, gc, dc):
        X = np.random.rand(n).astype(np.int32)

        def sum_op(X):
            return [np.sum(X)]

        op = core.CreateOperator(
            "SumElementsInt",
            ["X"],
            ["y"]
        )

        self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=sum_op,
        )

    @given(n=st.integers(1, 65536),
           dtype=st.sampled_from([np.float32, np.float16]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_elementwise_sqrsum(self, n, dtype, gc, dc):
        if dtype == np.float16:
            # fp16 is only supported with CUDA/HIP
            assume(gc.device_type == workspace.GpuDeviceType)
            dc = [d for d in dc if d.device_type == workspace.GpuDeviceType]

        X = np.random.rand(n).astype(dtype)

        def sumsqr_op(X):
            return [np.sum(X * X)]

        op = core.CreateOperator(
            "SumSqrElements",
            ["X"],
            ["y"]
        )

        threshold = 0.01 if dtype == np.float16 else 0.005

        self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=sumsqr_op,
            threshold=threshold,
        )

    @given(n=st.integers(5, 8), **hu.gcs)
    def test_elementwise_avg(self, n, gc, dc):
        X = np.random.rand(n).astype(np.float32)

        def avg_op(X):
            return [np.mean(X)]

        op = core.CreateOperator(
            "SumElements",
            ["X"],
            ["y"],
            average=1
        )

        self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=avg_op,
        )

        self.assertGradientChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            outputs_to_check=0,
            outputs_with_grads=[0],
        )

    @serial.given(batch_size=st.integers(1, 3),
           m=st.integers(1, 3),
           n=st.integers(1, 4),
           **hu.gcs)
    def test_rowwise_max(self, batch_size, m, n, gc, dc):
        X = np.random.rand(batch_size, m, n).astype(np.float32)

        def rowwise_max(X):
            return [np.max(X, axis=2)]

        op = core.CreateOperator(
            "RowwiseMax",
            ["x"],
            ["y"]
        )

        self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=rowwise_max,
        )

    @serial.given(batch_size=st.integers(1, 3),
           m=st.integers(1, 3),
           n=st.integers(1, 4),
           **hu.gcs)
    def test_columnwise_max(self, batch_size, m, n, gc, dc):
        X = np.random.rand(batch_size, m, n).astype(np.float32)

        def columnwise_max(X):
            return [np.max(X, axis=1)]

        op = core.CreateOperator(
            "ColwiseMax",
            ["x"],
            ["y"]
        )

        self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=columnwise_max,
        )

        # Test shape inference logic
        net = core.Net("test_shape_inference")
        workspace.FeedBlob("x", X)
        output = net.ColwiseMax(["x"], ["y"])
        (shapes, types) = workspace.InferShapesAndTypes([net])
        workspace.RunNetOnce(net)

        self.assertEqual(shapes[output], list(workspace.blobs[output].shape))
        self.assertEqual(shapes[output], [X.shape[0]] + [X.shape[2]])
        self.assertEqual(types[output], core.DataType.FLOAT)